Obtaining fault tolerance avoidance behavior using deep reinforcement learning

Author:

Aznar FidelORCID,Pujol Mar,Rizo Ramón

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference33 articles.

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2. From perception to decision: a data-driven approach to end-to-end motion planning for autonomous ground robots;Pfeiffer,2017

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5. Autonomic sonar sensor fault manager for mobile robots;Doran;World Acad. Sci. Eng. Technol. Int. J. Comput. Inf. Eng.,2017

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1. Euclidean distance damping–based adaptive sliding mode fault-tolerant event-triggered trajectory-tracking control;Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering;2022-10-10

2. RSAC: A Robust Deep Reinforcement Learning Strategy for Dimensionality Perturbation;IEEE Transactions on Emerging Topics in Computational Intelligence;2022-10

3. Deep reinforcement learning based mobile robot navigation: A review;Tsinghua Science and Technology;2021-10

4. End-to-End Deep Fault Tolerant Control;IEEE/ASME Transactions on Mechatronics;2021

5. Goal-directed autonomous navigation of mobile robot based on the principle of neuromodulation;Network: Computation in Neural Systems;2019-09-30

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